EVALUATION OF CROSS-LINKING TIME FOR POROUS GELATIN HYDROGELS ON CELL SHEET DELIVERY PERFORMANCE
Bibliographic record
Abstract
To overcome the drawbacks posed by surgical manipulation of bioengineered corneal endothelial cell (CEC) sheets, a simple stirring process combined with freeze-drying method was recently developed for the production of cross-linked porous gelatin hydrogels that can provide the support structure and improve the aqueous humor circulation. In this study, we further evaluated the influence of cross-linking time (0–48 h) on the delivery performance of carbodiimide modified gelatin carriers. It was found that smaller pore size, lower porosity, and larger superficial area were associated with increasing extent of cross-linking of the carrier discs. Although the hydrogels treated for short reaction time (i.e., below 6 h) had low resistance to initial nutrient permeation, these materials exhibited rapid swelling, implying a potential anterior segment tissue squeezing effect for use as intraocular implants. In addition, the delivery carriers with limited extent of cross-linking degraded too fast to be effective for retention of cell sheet grafts at the site of injury. By contrast, the gelatin samples with cross-linking degrees greater than 50% showed slower degradation rates and smaller porous structure, thereby possibly causing a significant inhibition of CEC proliferation. Cell sheet transfer studies demonstrated that the carrier discs with a high cross-linking degree (59.4 ± 1.3%) were more difficult to achieve stable cell attachment than their counterparts with a low cross-linking degree (48.3 ± 1.5%). Our findings suggest that among the cross-linked porous samples studied, 12 h is the best cross-linking reaction time for preparation of cell sheet carriers with suitable delivery performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".